Externalising Behaviours, Impulsivity, Alexithymia, and Emotional Dysregulation in Adolescents' Suicidality.
Bibliographic record
Abstract
Objective: suicide represents the second leading cause of death among adolescents (WHO, 2021). A deeper understanding of the characteristics that lead to it is crucial to increase the ability of clinicians in evaluating, treating, and preventing it. The objective of this study is to analyze the differences in impulsivity, externalizing behaviors, emotion dysregulation, and alexithymia between two groups of adolescents, the first presenting suicidal ideation (SI), the second presenting at least one suicide attempt (SA), in an ideation-to-action framework. Method: = 93). All were hospitalized in the Complex Operative Child Neuropsychiatry Hospital Unit (UOC-NPI) of the Hospital-University of Padua. Data were collected using the Youth Self-Report (YSR 11-18), Barratt's Impulsiveness Scale (BIS-11) and the Toronto Alexithymia Scale (TAS-20) questionnaires. Results: the SA group obtained higher clinical scores in the YSR "rule-breaking behavior" and "conduct problems" scales, and in total TAS-20. Conclusions: the role of externalizing problems and alexithymia could open new frontiers in the understanding of suicide. These new data could be useful for the implementation of early screening protocols and for directing clinical interventions, promoting greater emotion regulation and anger management skills among patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".